جست‌وجوی استاد و پژوهشگر

• Intelligent Robotics
• Multi-Robot Systems
• Human-Robot Interaction
• +۳ مورد دیگر

Sérgio Paulo Carvalho Monteiro is an Assistant Professor in the Department of Industrial Electronics at the School of Engineering, University of Minho, and a Senior Researcher at Centro Algoritmi. He serves as Director of the Doctorate in Electronics and Computer Engineering since 2021 and previously led the Integrated Masters program (2011-2013), with core expertise in robotics and signal processing education. His academic credentials include a Doctor of Engineering (DEng), Master of Science (MSc), and PhD. Monteiro's research centers on intelligent robotics with dual focus on single-robot autonomy and multi-robot coordination . His work pioneers dynamic neural field approaches for human-vehicle interaction, particularly in learning driver routines through machine learning. Key application domains span industrial logistics , autonomous transportation , and collaborative workstations , emphasizing safety in human-robot shared environments. Analysis of his 42 publications reveals a clear trajectory from foundational formation control (2002-2010) toward contemporary AI-driven solutions for intelligent cockpits (2020-2024), with increasing emphasis on human-centered design and real-world industrial implementation. He has secured funding through major European initiatives including Horizon 2020 (iFACTORY), FP6 (CoopDyn), and national FCT projects, focusing on autonomous logistics and multi-robot collaboration. His academic leadership extends to directing doctoral programs and shaping curriculum for over 500 engineering students. As core member of the Mobile and Anthropomorphic Robotics Lab within Centro Algoritmi's CAR R&D Group, he develops bimanual manipulators (RAMBO) and safety-critical systems for human-robot collaboration, with recent expansion to CCG/ZGDV Institute affiliation (2023).

Youness Diouaneمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Computational Biology
• Network Theory
• +۴ مورد دیگر

Youness Diouane is a Postdoctoral Research Fellow at Boston College in the Department of Computer Science, collaborating with Prof. George Mohler and Prof. Jose Bento. His research focuses on network-based models for Artificial Intelligence (AI) and computational biology, emphasizing mathematical properties of network alignment algorithms. Education Ph.D. in Statistical Field Theory, Scuola Internazionale Superiore di Studi Avanzati (SISSA), Italy (2022) M.S. in Condensed Matter and Statistical Physics, International Center for Theoretical Physics (ICTP), Italy (2018) M.S. in Quantum Information Theory, Faculty of Sciences, Rabat, Morocco (2017) His interdisciplinary work spans network alignment, spatial-temporal point process modeling, fairness-aware machine learning in criminal justice, and synthetic control methods. Publications reflect expertise in Hawkes processes, critical phenomena, and statistical field theory. He is active in applying mathematical models to public health and societal challenges.

Inger Skredeمشاهده پروفایل

استاد

• Speciation
• Population genomics
• Comparative fungal genomics
• +۷ مورد دیگر

Inger Skrede is a Professor in the Section for Genetics and Evolutionary Biology at the University of Oslo's Faculty of Mathematics and Natural Sciences. Her research spans fungal evolutionary biology, population genomics, and fungal ecology, with particular focus on speciation mechanisms and wood decay processes. Skrede's research interests center on fungal evolution and ecology, with emphasis on speciation mechanisms in Trichaptum abietinum , evolutionary processes leading to functional transitions like efficient wood decay and ectomycorrhizal symbiosis, and brown rot decay in Serpula lacrymans . She also investigates fungal systematics in the ascomycete family Helvellaceae , fungal diversity in built environments, responses to modified wood, and conservation biology of rare fungal wood decay species. Her work integrates population genomics, comparative genomics, and phylogenetic approaches to understand evolutionary processes in fungi. Analysis of her recent publications reveals a strong focus on fungal genomics and ecology, with particular attention to how fungi adapt to different environments including built structures and forest ecosystems. Her research demonstrates how genomic tools can illuminate speciation mechanisms, adaptive processes, and the ecological roles of fungi in diverse habitats ranging from natural forests to human-built environments. Skrede leads multiple research projects including Characterization of yeast biodiversity from Oslo and Kveik beer, Effects of polyploidization during adaptive evolution in yeasts (PloidYeast), Fungal Conservation Genetics, and The genomic and physiological basis of invasiveness in a harmful house-invader (InHouse). She is actively involved with the Centre for Integrative Microbial Evolution (CIME) and the Oslo Mycology Group (OMG), contributing to collaborative research efforts in fungal biology and evolution.

• Human-Computer Interaction
• Image Processing
• Digital Signal Processing
• +۴ مورد دیگر

Yoichi Kato serves as a Professor (without tenure) at Waseda University's School of Creative Science and Engineering and Global Center for Science and Engineering. He maintains additional affiliations with the Graduate School of Creative Science and Engineering and the Global Education Center. His academic profile spans decades of research and teaching in information and communication technologies, with particular expertise in video coding algorithms, image processing, and human-computer interaction systems. Professor Kato's research interests focus on Human interface and interaction, Image Processing, Digital Signal Processing, and Design Thinking. His scholarly trajectory demonstrates a progression from foundational work in signal processing during the 1980s-1990s toward more applied, interdisciplinary research in recent years. Early publications established his expertise in motion picture coding algorithms and image restoration techniques, while his current work explores IoT applications, machine learning prototyping methods, and user-centered design approaches. Analysis of his publication history reveals significant contributions to video compression standards and image processing algorithms, with recent research expanding into collaborative systems, disaster prevention technologies, and educational applications. His work bridges theoretical foundations with practical implementations, particularly evident in his recent projects involving drone-based terrain analysis and automated plant monitoring systems. Professor Kato actively supervises graduate students through Master's Thesis projects in the Department of Modern Mechanical Engineering and Research on Neuro Robotics. His teaching portfolio includes diverse courses such as SHIP Field Practice, Modern Information and Communication Systems, Exercise for Co-Creation workshop, and Introduction to Python Programming, reflecting his interdisciplinary approach to education. His laboratory activities center on practical applications of information technologies, with current projects examining drone applications for disaster prevention, remote plant monitoring systems using IoT technology, and prototyping methodologies for machine learning applications. These initiatives demonstrate his commitment to translating theoretical knowledge into real-world solutions that address contemporary challenges.

• Information Technology
• Communication Engineering
• Image Processing
• +۳ مورد دیگر

Hiroshi Watanabe is a Professor at Waseda University 's Department of Communications and Computer Engineering , School of Fundamental Science and Engineering. With a Doctor of Engineering from Hokkaido University (1985), his career spans NTT Human Interface Laboratories (1985-2000) and academic leadership at Waseda since 2000, including chairing ISO/IEC JTC 1/SC 29 (1999-2006). A Fellow of IEEE , IEICE , and other institutions, he focuses on image/video coding , machine vision , and multimedia distribution . Education: BE, ME, Ph.D. in Electronic Engineering (Hokkaido University) Professional Affiliations: IEEE, IEICE, IPSJ, ITE, IIEEJ His research bridges image/video processing with deep learning , emphasizing machine-centric coding , real-time detection , and multimodal analysis . Key recent work includes: Novel image coding frameworks combining edge learning and diffusion models Super-resolution techniques for QR codes and medical imaging Advanced object detection using non-local modules and attention networks 3D pose estimation and point cloud analysis for robotics and healthcare He has received prestigious awards including the 2005 Information Processing Society of Japan Standardization Contribution Award and multiple earlier honors. His recent publications (2024-2025) demonstrate leadership in machine vision coding , real-time medical detection , and multimodal signal processing , often integrating stable diffusion , feature fusion , and parameter-efficient models .

Evan McHughes Palmerمشاهده پروفایل

دانشیار

• Cognitive Science
• Human Factors
• Vision
• +۶ مورد دیگر

Evan McHughes Palmer is an Associate Professor and Chair in the Department of Psychology at San Jose State University's College of Social Sciences. His interdisciplinary research integrates cognitive science with human factors, focusing on the interaction between modern technologies and human cognition, particularly in visual perception, attention, and training methodologies. Academic Focus: Cognitive Science, Human Factors, Vision Science Key Research Areas: Gamification, Medical Image Perception, Road Hazard Detection His recent publications highlight trends in visual attention dynamics, perceptual learning applications, and technology-mediated cognitive training across domains like healthcare and transportation. Research emphasizes quantifying cognitive processes through eye movement analysis, workload measurement, and attentional capture mechanisms. Palmer's lab investigates how gamification elements (points, badges, leaderboards) enhance motivation and performance in repetitive tasks. This aligns with broader interests in human-technology interaction, including web advertising blindness, medical diagnostics, and aviation interface design. His work bridges theoretical cognitive science with applied human factors solutions.

• Formal verification
• Algorithmic game theory
• Infinite-state systems
• +۹ مورد دیگر

Patrick Totzke is a Professor in the Department of Computer Science at the University of Liverpool’s School of Electrical Engineering, Electronics and Computer Science. As a leading researcher at the intersection of mathematics and computer science, he specializes in the foundations of formal verification, algorithmic game theory, and infinite-state systems. Education: While specific degrees are not listed, his expertise and role as Professor strongly suggest advanced graduate training in computer science and mathematics. Research Interests: Algorithmic game theory, particularly strategy complexity and games on infinite graphs Decidability and complexity of verification problems such as bisimulation, language inclusion, model checking, and synthesis Counter automata, vector addition systems, Petri nets, and process algebras Computational logics with fixed-points, temporal or probabilistic modalities, and associated games Real-time systems including timed automata, timed Petri nets, and timed games His recent focus centers on timed automata and stochastic games on graphs. Research Grants: Below the Branches of Universal Trees – EPSRC (March 2023 – August 2024) Unambiguity in Infinite-state Systems – Royal Society (March 2021 – March 2023) COSTRA: The Cost of Winning Strategies – EPSRC (July 2021 – September 2024) Teaching: Module Co-ordinator for COMP122 Object-Oriented Programming (2024–25). Labs & Teams: While no specific lab names are provided, his grants and publications indicate active leadership of research teams in formal methods and verification.

Markus Steinbachمشاهده پروفایل

استاد

• German Grammar
• Semantics
• Pragmatics
• +۸ مورد دیگر

Markus Steinbach is a Professor of German Linguistics at the University of Göttingen, specializing in theoretical and experimental linguistics with a focus on sign languages, spoken language interfaces, and discourse structures. He serves as Spokesperson of the Göttingen Centre 'Text Structures' (2021–present) and has held prestigious fellowships, including a 2017 Distinguished Guest Scientists Fellowship at the Hungarian Academy of Sciences. Core research areas: German grammar, sign languages, syntax-semantics interface, grammaticalization. Active projects: TypMod (CNRS), Parts of Speech and Iconicity in DGS (DFG), IDEALISM (DFG-AHRC), and Sign Hub (Horizon 2020). His recent work explores temporal and causal pragmatics, polar response strategies, and neurophysiological aspects of sign language processing. Awards include a 2017 MTA fellowship, and he contributes to editorial leadership via co-editing Linguistische Berichte (since 2014) and the Sign Languages and Deaf Communities series (since 2012). Collaborations span institutions like the DFG and CNRS.

• Geodesy
• GNSS Technology
• Remote Sensing
• +۳ مورد دیگر

Jacek Rapiński is a Professor at the University of Warmia and Mazury (UWM) in the Department of Geoengineering. His research focuses on geodesy, GNSS technology, LiDAR data processing, and structural monitoring, particularly in mining and environmental contexts. He has supervised doctoral students at UWM and UPC and contributed to numerous publications. Over 58 publications in WoS/Scopus 307 citations (WoS/Scopus) H-index 12 (WoS/Scopus), 14 (Google Scholar) Supervised 1 doctoral student at UWM and 2 total His recent research explores GNSS multipath mitigation , low-cost sensor integration , and machine learning applications for infrastructure monitoring. Projects include innovative tools for GNSS mission planning and climate-focused land consolidation strategies. While no explicit awards are listed, his work involves advanced methodologies like Msplit estimation and MEMS sensor fusion . He emphasizes promoting doctoral research with available funding and infrastructure at UWM.

Cedric Vegaمشاهده پروفایل

پژوهشگر

• Remote Sensing
• Forest Ecology
• Lidar Technology
• +۵ مورد دیگر

Cedric Vega is a Research Director at the National Institute of Geographic and Forest Information (IGN) in France, where he leads research in the Laboratory of Forest Inventory (LIF) since March 2014. His work focuses on developing advanced methodologies for forest monitoring using remote sensing technologies, particularly lidar, photogrammetry, and satellite imagery. He has established himself as a leading expert in integrating National Forest Inventory data with remote sensing sources to create high-resolution forest attribute maps across France. Dr. Vega completed his education with a focus on Environmental Sciences at the University of Quebec in Montreal (2000-2006) and Ecology at Université Toulouse III - Paul Sabatier (1999-2000). His professional journey includes significant international experience as Head of the Laboratory of Applied Informatics and Geomatics at the Institut Français de Pondichéry in India (2010-2014) and teaching positions in Lidar remote sensing at AgroParisTech. Vega's research interests center on remote sensing of forest ecosystems, with particular emphasis on developing methods for monitoring forest structure and dynamics using lidar, photogrammetry, and very high-resolution optical imagery. His work addresses critical challenges in estimating forest biophysical parameters, mapping forest attributes at high spatial resolution, and improving the accuracy of national forest inventories through multisource approaches. Recent research increasingly incorporates machine learning and deep learning techniques to process complex remote sensing data streams. Analysis of his recent publications reveals a strong trend toward integrating multiple remote sensing data sources (particularly GEDI, Sentinel-1, and Sentinel-2) with traditional forest inventory data to create high-resolution forest attribute maps. His work demonstrates consistent innovation in solving spatial misalignment issues between satellite measurements and ground plots, improving geolocation accuracy, and developing robust small-area estimation techniques for forest management applications. Vega has established significant collaborations across European research institutions, particularly with French National Institute for Agriculture, Food, and Environment (INRAE), and has contributed to advancing forest monitoring methodologies that support carbon stock assessment and climate change adaptation strategies. As a Research Director at IGN, Vega leads the Laboratory of Forest Inventory which serves as a critical hub for developing and implementing advanced forest monitoring techniques in France. His team works at the intersection of remote sensing technology and practical forest management needs, focusing on creating operational tools that can be deployed at national scale while maintaining statistical rigor.

• Computer Vision
• Pattern Recognition
• Human Behavior Understanding
• +۵ مورد دیگر

Anis KACEM is a Researcher at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) within the University of Luxembourg, part of the Signal Processing and Satellite Communications (SIGCOM) research group. His work focuses on Computer Vision and Pattern Recognition, particularly in Human Behavior Understanding from visual data. He received his PhD in Computer Science from the University of Lille (France) in 2018. Research interests include advanced topics such as Earth Observation via multi-modal autoencoders, domain adaptation for image classification, vulnerability-aware deepfake detection, and CAD system reverse engineering. His contributions span neural network pruning, 3D shape analysis, and generative models for medical imaging. Publications highlight innovations in spatio-temporal learning for deepfake detection, hybrid attention mechanisms for pedestrian detection, and tool-augmented CAD task solvers. His work bridges theoretical advances with practical applications in autonomous systems and space technology. Notably, he has contributed to challenges like the SHARP 2023 Challenge on CAD history recovery and developed frameworks like Picasso for CAD sketch inference using self-supervised learning.

• Trustworthy AI
• AI Verification
• Explainable AI
• +۵ مورد دیگر

Professor Xiaowei Huang serves as Professor of Computer Science at the University of Liverpool within the School of Electrical Engineering, Electronics and Computer Science. He leads the Trustworthy Autonomous Cyber Physical System Lab, focusing on critical research at the intersection of machine learning, formal methods, and robotics. His work addresses fundamental challenges in autonomous systems that learn, adapt, and make decisions independently. Dr. Huang's research interests center on trustworthy AI with specific expertise in verification, explainable AI, and AI safety and security. His group investigates autonomous systems' properties including safety, robustness, trustworthiness, and security to determine their applicability in safety-critical environments. This encompasses neural network verification, practical analysis techniques for machine learning, deep learning interpretation, and logic-based approaches for multi-agent autonomous systems. His recent publications reveal strong trends in autonomous driving safety verification, robust computer vision systems, and neural-symbolic integration. The research spans multiple application domains including self-driving cars, underwater vehicles, robotics, and healthcare systems where safety and interpretability are paramount. His work demonstrates consistent focus on practical verification frameworks and robustness assessment methodologies. As Principal Investigator or Liverpool PI, Dr. Huang has secured over £1.86M in research funding from prestigious sources including Dstl, EPSRC, and the European Commission, with additional co-investigator roles totaling over £15M. Current projects include Safety Assurance for Autonomous Underwater Vehicles and Test Coverage Metrics for AI. He directs the Autonomous Cyber Physical Systems Laboratory, soon to be relocated to the new Digital Innovation Facility Building. Dr. Huang serves as Module Co-ordinator for Advanced Artificial Intelligence (COMP219) and supervises numerous PhD students working on topics including uncertainty analysis in data, autonomous vehicle perception, graph neural networks, and reliable deep learning models. His laboratory provides a research environment focused on bridging theoretical foundations with practical safety-critical applications.

Koushik Sinhaمشاهده پروفایل

دانشیار

• Mobile Computing
• Wireless Ad hoc Networks
• Wireless Sensor Networks
• +۶ مورد دیگر

Koushik Sinha is an Associate Professor at the School of Computing, Southern Illinois University Carbondale, where he directs the Complex Networks & Applications Lab. With extensive experience spanning academia and industry—including roles at Qatar Computing Research Institute, Hewlett-Packard Labs, and Honeywell—he brings practical expertise to his research in wireless networking, complex systems, and human-machine collaboration. His work bridges theoretical foundations with real-world applications across multiple domains. Education Ph.D. in Computer Science, Jadavpur University, India (2007) M.S. in Computer Science, Clemson University, USA (2003) B.Tech. in Computer Science, Kalyani Government Engineering College, India (2001) Research Focus Dr. Sinha's research centers on energy-efficient wireless communication protocols, 5G/6G networking, and complex network analysis. His "Communication through Silence" paradigm represents a novel approach to minimizing energy consumption in wireless networks. Current projects address critical challenges in ultra-dense 5G deployments, smart city infrastructure, and the CrowdCloud platform for hybrid human-machine computing. His work increasingly intersects with societal challenges in rural connectivity, agricultural technology, and public health. Publication Trends Analysis of Dr. Sinha's recent publications reveals a strategic progression from core wireless networking research toward interdisciplinary applications. While maintaining expertise in energy-efficient protocols and network optimization, his work now addresses smart city infrastructure, rural broadband expansion, precision agriculture, and pandemic response systems. His publications demonstrate growing integration of machine learning techniques and increasing focus on real-world impact in underserved communities. Scientific Recognition Senior Member, IEEE Grand Prize Winner of 9th OSS World Challenge for AIDR (2015) N.V. Gadadhar Memorial Award from IETE (2011) Young Scientist Award from Indian Science Congress Association (2009) Mentorship and Leadership Dr. Sinha has successfully guided multiple Ph.D. and MS students through research in wireless networking and complex systems. His leadership extends to conference organization as General Co-Chair for IEEE ANTS 2018 and TPC Co-Chair for previous editions. Within his department, he has shaped curriculum as Course Coordinator for key offerings including Distributed Systems, Mobile and Wireless Computing, and Computer Architecture. Research Infrastructure The Complex Networks & Applications Lab under Dr. Sinha's direction supports cutting-edge research in wireless protocols, network optimization, and hybrid computing systems. Current projects include 5G network optimization, ultra-dense base station placement, and the CrowdCloud platform for human-machine collaborative computing, providing students with opportunities to address real-world challenges in smart cities, rural connectivity, and emergency response systems.

Peter Bonczمشاهده پروفایل

استاد

• Database Systems
• Column-Store Databases
• Vectorized Query Processing
• +۵ مورد دیگر

Peter Boncz is a Professor in the special chair of Large Scale Analytical Database Systems at Vrije Universiteit Amsterdam and leads the Database Architectures (DA) research group at CWI (Centrum Wiskunde & Informatica), the Netherlands' national research institute for mathematics and computer science. He serves on the CWI management team and is actively involved in multiple research initiatives and industry collaborations. Professor Boncz is internationally recognized as a pioneer of column-store databases, introduced through his PhD project MonetDB. His research spans database architecture, query processing optimization, and analytical database systems. His work on vectorized query processing with his first PhD student Marcin Zukowski has become foundational in modern analytical databases including BigQuery, Databricks, Snowflake, and DuckDB, which has millions of monthly downloads. Current research focuses include GPU data processing, vector search optimization, confidential computing, and graph data management. Boncz's recent publications reveal strong trends toward optimizing database systems for modern hardware architectures, particularly GPUs and cloud CPUs. His work bridges theoretical database concepts with practical implementation, focusing on performance optimization through innovative data layouts, compression techniques, and hardware-aware processing. The research shows a clear trajectory from foundational database concepts toward specialized optimization for emerging hardware and application requirements. VLDB Test of Time Award 2025 (second time, previously won in 2009) CIDR Test of Time Award 2024 ACM Fellow (2022) Humboldt Research Award (2013) ICTRegie Award (2006) Boncz has co-founded six spin-off companies in data systems, including MonetDB BV, and serves as an advisor to ventures like Databricks Corp. His research is supported by multiple external funding projects including Actian Research Grants, Databricks research agreements, and Motherduck Service Agreements. He has advised numerous students, with Marcin Zukowski being notably mentioned as his first PhD student who co-developed vectorized query processing. As leader of the Database Architectures research group at CWI, Boncz oversees a team focused on pushing the boundaries of database technology. The group maintains close ties with industry through projects with Databricks, Motherduck, and RelationalAI, while continuing to develop open-source technologies like DuckDB. The team is particularly active in GPU acceleration, confidential computing, and graph data management through the Linked Data Benchmark Council (LDBC), which Boncz founded.

• Reinforcement Learning
• Stochastic Optimization

Daniil Nikolaevich Tyapkin is a Junior Research Fellow and Postgraduate Student at the National Research University Higher School of Economics (HSE), affiliated with the Faculty of Computer Science. He works within the Institute of Artificial Intelligence and Digital Sciences and the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis under the supervision of Alexey Alexandrovich Naumov. Education: 2021: Bachelor's degree in Applied Mathematics and Computer Science from HSE Currently in 2nd year of postgraduate studies with dissertation topic: "Randomized trial for reinforcement learning" Research Interests: His work focuses on reinforcement learning and stochastic optimization, including exploration strategies, generative flow networks, and theoretical analysis of learning algorithms. He develops efficient methods for complex decision-making problems with applications in AI and data science. Publications: His research appears in top venues (ICLR, NeurIPS, ICML, AISTATS) covering reinforcement learning, optimization, and probabilistic modeling. Recent works explore GFlowNets, entropy regularization, and high-probability bounds for learning algorithms. Awards & Honors: Letter of gratitude from HSE Faculty of Computer Science (2022) Publication bonus for List A journal articles (2023-2024) HSE Personnel Reserve - New Researchers category (2022-2023) Teaching & Advising: Teaches courses in Mathematical Statistics and Matrix Computations. No formal student advising mentioned. Laboratory Affiliation: Member of the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis, focusing on algorithmic development for machine learning challenges.